Perception and sensor fusion are important processes of fully or partially Automatic Driving vehicles. They can be considered the eyes and ears of vehicles with some level of automation. The quality of their output sets a boundary on the quality and safety that the subsequent behaviors such as route planning and driving components can achieve.
Initial approaches to solving the perception and sensor fusion problem were based on detecting and tracking objects using a bounding box approach; but the bounding box approach fails to take into account large objects, such as buildings for which no bounding box can be constructed; in addition, the bounding box approach is computationally very expensive. Particle filters in conjunction with Dynamic Occupancy Grids (DOG) address some of the problems of the bounding box technology: particle filters and DOG model the space around the vehicle with a grid, sensor measurements are transformed in particles that are placed on the grid and then abstracted to recognize object boundaries and road obstacles. As a result, static and dynamic objects can be represented simultaneously independently of their size, overcoming some of the limitations of the bounding box technology.
Particle filters and Dynamic Occupancy Grids are effective at detecting both dynamic objects, such as other moving vehicles, and static objects, such as large buildings, but they still suffer from a high computational cost. Specifically, Particle filters require the use of a large number of particles on the Dynamic Occupancy Grids; since particles have to be tracked individually to monitor the development of the space around the vehicle, the computational cost of particle filters increases with the number of particles. Furthermore, factors such as vehicles speed may further increase the number of particles that need to be tracked.
In realistic systems, the use of particle filters forces a trade-off between the precision of the description of the environment, which improves with an increase in the number of particles, and response time which improves with a decrease in the number of particles.